A method is provided for estimating past data by identifying a high frequency data set for a defined time period. A pattern is calculated for the high frequency data set and then the pattern is applied to a low frequency data set in a past time period to estimate a high frequency query point.
Legal claims defining the scope of protection, as filed with the USPTO.
1. A system comprising: a processor; a metric data store to store fine granular metric data sets and coarse granular metric data sets; and a memory device including instructions that, when executed by the processor, cause the system to: identify in the metric data store a plurality of fine granular metric data sets within multiple defined time periods which are related; calculate slopes for individual fine granular metric data sets included in the plurality of fine granular metric data sets identified; correlate the slopes for the individual fine granular metric data sets to form a correlated slope; receive, from a client, a query identifying a data query point in a defined time period in the course granular metric data sets; apply, for the defined time period, the correlated slope to the course granular metric data sets accessed from the metric data store to generate an estimate of the data query point for data that was discarded within the course granular metric data sets in the metric data store for the defined time period; and send a response to the client for the defined time period using the estimate of the data query point.
2. The system as in claim 1 , wherein the fine granular metric data sets are discarded from the metric data store over time according to a retention policy in favor of the coarse granular metric data sets.
3. The system as in claim 1 , wherein, the fine granular metric data sets are captured at a first interval rate; and the coarse granular metric data sets are captured at a second interval rate, wherein the first interval rate is greater than the second interval rate.
4. The system as in claim 1 , wherein each coarse granular metric data set comprises an aggregation of multiple fine granular metric data sets which are related and have been summarized.
5. A system comprising: a processor; a data store to store a plurality of data sets; and a memory device including instructions that, when executed by the processor, cause the system to: identify, in the data store, a plurality of fine granular data sets within multiple defined time periods which are related; calculate slopes for individual fine granular data sets included in the plurality of fine granular data sets identified; correlate the slopes of the plurality of fine granular data sets to form a correlated slope; receive, from a client, a query identifying a data query point in a past time period in a course granular metric data set; apply, for the past time period, the correlated slope to the course granular metric data set to generate an estimate of the data query point for data that was discarded for the past time period; and send a response to the client for the past time period using the estimate of the data query point.
6. The system as in claim 5 , wherein the memory device includes instructions that, when executed by the processor, causes the system to: calculate a trend for the correlated slope; and estimating the data query point using the correlated slope verified based on the trend calculated.
7. The system as in claim 5 , wherein the memory device includes instructions that, when executed by the processor, causes the system to: determine a seasonality of the plurality of fine granular data sets identified; and estimate the data query point using the correlated slope modified based on the seasonality determined.
8. The system as in claim 5 , wherein the memory device includes instructions that, when executed by the processor, causes the system to: calculate a maximum bound limit and a minimum bound limit of the course granular data; and estimate the data query point using the correlated slope and further using the maximum bound limit and the minimum bound limit.
9. The system as in claim 8 , wherein the maximum bound limit is used as a constraint on the correlated slope preventing the correlated slope from exceeding the maximum bound limit.
10. The system as in claim 8 , wherein the minimum bound limit is used as a constraint on the correlated slope preventing the correlated slope from exceeding the minimum bound limit.
11. The system as in claim 8 , wherein the memory device includes instructions that, when executed by the processor, causes the system to: calculate a mean for the maximum bound limit and the minimum bound limit; and apply the mean as an anchor on the correlated slope.
12. The system as in claim 5 , wherein the memory device includes instructions that, when executed by the processor, causes the system to: calculate slopes for individual fine granular data sets according to a linear function, disjoint function, second order function, or third order function.
13. The system as in claim 5 , wherein the memory device includes instructions that, when executed by the processor, causes the system to: correlate the slopes of the plurality of fine granular data sets to form a correlated slope based on a mean of the slopes, a highest slope, a lowest slope, or an interpolation of the slopes.
14. A computer-implemented method, comprising: identifying a plurality of fine granular metric data sets within multiple defined time periods which are related; calculating slopes for individual fine granular metric data sets included in the plurality of fine granular metric data sets identified, using a processor; correlating the slopes for the individual fine granular metric data sets to form a correlated slope; receiving, from a client, a query identifying a data query point in a past time period in a course granular metric data set; applying, for the past time period, the correlated slope to the course granular metric data set to generate an estimate of the data query point for data that was discarded for the past time period, using the processor; and sending a response to the client for the past time period using the estimate of the data query point.
15. The method of claim 14 , further comprising: identifying a coarse granular data set using one or more of the plurality of fine granular data sets; and storing the coarse granular data set in a data store.
16. The method of claim 15 , wherein the coarse granular data set comprises an aggregation of two or more of the plurality of fine granular data sets that have been summarized.
17. The method of claim 15 , wherein the coarse granular data set comprises data captured at a less frequent interval than the fine granular data.
18. The method of claim 14 , further comprising: access the coarse granular data set in a data store; access the correlated slope in the data store.
19. The method of claim 14 , wherein the fine granular data set and the coarse granular data set comprise metric data of one or more computing devices.
20. The method of claim 19 , wherein the metric data includes website access data, web server load data, purchase order placement data, website hit data, page view data, retail order data, transaction request data, session data, bounce rate data, average page depth data, click data, central processor unit load data, average queue size data, average request size data, input/output status data, wait time data, utilization data, bound data, buffer data, or cache data.
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March 29, 2017
August 20, 2019
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